Majority-inverter graphs (MIGs) are a logic representation with remarkable algebraic and Boolean properties that enable efficient logic optimizations beyond the capabilities of traditional logic representations. Further, since many nano-emerging technologies, such as quantum-dot cellular automata (QCA) or spin torque majority gates (STMG), are inherently majority-based, MIGs serve as a natural logic representation to map into these technologies. So far, MIG optimization methods predominantly target to reduce the depth of the logic networks, corresponding to low delay implementations in the respective technologies. In this paper, we introduce several methods to optimize the size of MIGs. They can be applied such that the depth of the logic network is preserved; therefore our methods have a direct effect on the physical area, without worsening the delay. Some methods are inspired by existing size optimization algorithms for non-majority-based logic networks, others make explicit use of the majority function and its properties. All methods are Boolean-in contrast to algebraic optimization methods-which has a positive effect on the quality but challenges their implementation. Our experiments show that using our methods the size of MIGs in the EPFL combinational benchmark suite can be reduced by up to 7.12%. When mapped to QCA and STMG technologies we reduce the average area-delay-energy product by 2.31% and 2.07%, respectively.